Abstract Accurate prediction of precipitation type is an important part of weather forecasting. But using meteorological insight to make such predictions from a small set of weather variables achieves only limited success. We use correlation-based feature selection to assemble an effective subset of the large number of weather variables available in short-range weather forecasts, and from these we obtain the coefficients for multinomial regression, which can then be used to predict precipitation type. We applied this approach to data for significant locations in South Korea, obtained from the European Centre for Medium-Range Weather Forecasts and from the Regional Data Assimilation and Prediction System, and achieved predictions which are respectively 15% and 13% more accurate than those contained in the original forecasts.

中文翻译：

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使用基于相关性的特征选择和多项逻辑回归改进降水类型预测

摘要 降水类型的准确预测是天气预报的重要组成部分。但是，使用气象洞察力从一小组天气变量中做出这样的预测只能取得有限的成功。我们使用基于相关性的特征选择来组合短期天气预报中可用的大量天气变量的有效子集，并从中获得多项回归系数，然后可用于预测降水类型。我们将这种方法应用于韩国重要地点的数据，这些数据是从欧洲中期天气预报中心和区域数据同化和预测系统获得的，所获得的预测准确度分别比包含的预测高 15% 和 13%在最初的预测中。